References

Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Computer and Geosciences 10, 191–203.
Bishop C (2006) Pattern recognition and machine learning. Springer-Verlag https://www.microsoft.com/en-us/research/people/cmbishop/.
Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf.
Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman; Hall/CRC.
Campello RJGB, Moulavi D, Zimek A, Sander J (2015) Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data 10, 5:1–5:51.
Cena A, Gagolewski M (2020) Genie+OWA: Robustifying hierarchical clustering with OWA-based linkages. Information Sciences 520, 324–336.
Cortez P, Cerdeira A, Almeida F, Matos T, Reis J (2009) Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems 47, 547–553.
Deisenroth MP, Faisal AA, Ong CS (2020) Mathematics for machine learning. Cambridge University Press https://mml-book.com/.
Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise Proc. KDD’96, pp. 226–231.
Fletcher R (2008) Practical methods of optimization. Wiley.
Gagolewski M, Bartoszuk M, Cena A (2016) Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm. Information Sciences 363, 8–23.
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley.
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press https://www.deeplearningbook.org/.
Harper FM, Konstan JA (2015) The MovieLens datasets: History and context. ACM Transactions on Interactive Intelligent Systems 5, 19:1–19:19.
Hastie T, Tibshirani R, Friedman J (2017) The elements of statistical learning. Springer-Verlag https://web.stanford.edu/~hastie/ElemStatLearn/.
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22, 5–53. https://web.archive.org/web/20070306161407/http://web.engr.oregonstate.edu/~herlock/papers/eval_tois.pdf.
Hubert L, Arabie P (1985) Comparing partitions. Journal of Classification 2, 193–218.
James G, Witten D, Hastie T, Tibshirani R (2017) An introduction to statistical learning with applications in R. Springer-Verlag https://www.statlearning.com/.
Koren Y (2009) The BellKor solution to the Netflix grand prize. https://netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf.
Ling RF (1973) A probability theory of cluster analysis. Journal of the American Statistical Association 68, 159–164.
Lü L et al. (2012) Recommender systems. Physics Reports 519, 1–49. https://arxiv.org/pdf/1202.1112.pdf.
Müller AC, Nowozin S, Lampert CH (2012) Information theoretic clustering using minimum spanning trees Proc. German conference on pattern recognition, https://github.com/amueller/information-theoretic-mst.
Ng AY, Jordan MI, Weiss Y (2001) On spectral clustering: Analysis and an algorithm Proc. Advances in neural information processing systems 14 (NIPS’01), https://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf.
Nocedal J, Wright SJ (2006) Numerical optimization. Springer.
Peng RD (2019) R programming for data science. https://bookdown.org/rdpeng/rprogdatascience/.
Piotte M, Chabbert M (2009) The Pragmatic Theory solution to the Netflix grand prize. https://netflixprize.com/assets/GrandPrize2009_BPC_PragmaticTheory.pdf.
Quinlan R (1986) Induction of decision trees. Machine Learning 1, 81–106.
Quinlan R (1993) C4.5: Programs for machine learning. Morgan Kaufmann Publishers.
R Development Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria http://www.R-project.org.
Rezaei M, Fränti P (2016) Set-matching measures for external cluster validity. IEEE Transactions on Knowledge and Data Engineering 28, 2173–2186.
Ricci F, Rokach L, Shapira B, Kantor P (eds) (2011) Recommender systems handbook. Springer http://www.inf.unibz.it/~ricci/papers/intro-rec-sys-handbook.pdf.
Sarle WS et al. (eds) (2002) The comp.ai.neural-nets FAQ. http://www.faqs.org/faqs/ai-faq/neural-nets/part1/.
Simon D (2013) Evolutionary optimization algorithms: Biologically-inspired and population-based approaches to computer intelligence. Wiley.
Therneau TM, Atkinson EJ (2019) An introduction to recursive partitioning using the RPART routines. https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf.
Töscher A, Jahrer M, Bell RM (2009) The BigChaos solution to the Netflix grand prize. https://netflixprize.com/assets/GrandPrize2009_BPC_BigChaos.pdf.
Venables WN, Smith DM, R Core Team (2021) An introduction to R. https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.
Wickham H, Grolemund G (2017) R for data science. O’Reilly https://r4ds.had.co.nz/.
Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: An efficient data clustering method for large databases Proc. ACM SIGMOD international conference on management of data – SIGMOD’96, pp. 103–114.